{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyCaret 2 Clustering Example\n",
    "This notebook is created using PyCaret 2.0. Last updated : 31-07-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n"
     ]
    }
   ],
   "source": [
    "# check version\n",
    "from pycaret.utils import version\n",
    "version()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>2.004868</td>\n",
       "      <td>2.014602</td>\n",
       "      <td>2.071309</td>\n",
       "      <td>2.177712</td>\n",
       "      <td>2.331000</td>\n",
       "      <td>2.333596</td>\n",
       "      <td>2.588751</td>\n",
       "      <td>2.540238</td>\n",
       "      <td>2.450415</td>\n",
       "      <td>...</td>\n",
       "      <td>2.134281</td>\n",
       "      <td>2.133038</td>\n",
       "      <td>2.166872</td>\n",
       "      <td>2.101233</td>\n",
       "      <td>2.830067</td>\n",
       "      <td>2.489631</td>\n",
       "      <td>2.539570</td>\n",
       "      <td>2.711262</td>\n",
       "      <td>2.895427</td>\n",
       "      <td>3.073161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caribbean small states</td>\n",
       "      <td>2.801518</td>\n",
       "      <td>2.856684</td>\n",
       "      <td>2.997157</td>\n",
       "      <td>2.989451</td>\n",
       "      <td>2.767858</td>\n",
       "      <td>2.826752</td>\n",
       "      <td>2.679969</td>\n",
       "      <td>2.888693</td>\n",
       "      <td>2.740593</td>\n",
       "      <td>...</td>\n",
       "      <td>2.629580</td>\n",
       "      <td>2.650900</td>\n",
       "      <td>2.790665</td>\n",
       "      <td>2.822913</td>\n",
       "      <td>3.408651</td>\n",
       "      <td>3.264064</td>\n",
       "      <td>3.087653</td>\n",
       "      <td>3.314303</td>\n",
       "      <td>3.318432</td>\n",
       "      <td>3.260012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central Europe and the Baltics</td>\n",
       "      <td>4.678528</td>\n",
       "      <td>4.753209</td>\n",
       "      <td>4.604574</td>\n",
       "      <td>4.499988</td>\n",
       "      <td>4.679082</td>\n",
       "      <td>4.539711</td>\n",
       "      <td>4.666272</td>\n",
       "      <td>4.900196</td>\n",
       "      <td>5.100249</td>\n",
       "      <td>...</td>\n",
       "      <td>4.970861</td>\n",
       "      <td>4.841450</td>\n",
       "      <td>4.809057</td>\n",
       "      <td>5.054785</td>\n",
       "      <td>5.394921</td>\n",
       "      <td>5.284380</td>\n",
       "      <td>5.096212</td>\n",
       "      <td>5.041317</td>\n",
       "      <td>5.029266</td>\n",
       "      <td>5.017717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Early-demographic dividend</td>\n",
       "      <td>2.203164</td>\n",
       "      <td>2.156632</td>\n",
       "      <td>2.227311</td>\n",
       "      <td>2.364100</td>\n",
       "      <td>2.454394</td>\n",
       "      <td>2.450555</td>\n",
       "      <td>2.527081</td>\n",
       "      <td>2.347702</td>\n",
       "      <td>2.363263</td>\n",
       "      <td>...</td>\n",
       "      <td>2.337347</td>\n",
       "      <td>2.369884</td>\n",
       "      <td>2.385251</td>\n",
       "      <td>2.405126</td>\n",
       "      <td>2.701260</td>\n",
       "      <td>2.507131</td>\n",
       "      <td>2.495491</td>\n",
       "      <td>2.497340</td>\n",
       "      <td>2.586701</td>\n",
       "      <td>2.665603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>4.429090</td>\n",
       "      <td>4.203152</td>\n",
       "      <td>4.244351</td>\n",
       "      <td>4.453984</td>\n",
       "      <td>4.626920</td>\n",
       "      <td>4.688849</td>\n",
       "      <td>4.684790</td>\n",
       "      <td>4.613537</td>\n",
       "      <td>4.635098</td>\n",
       "      <td>...</td>\n",
       "      <td>4.566215</td>\n",
       "      <td>4.367146</td>\n",
       "      <td>4.297394</td>\n",
       "      <td>4.434848</td>\n",
       "      <td>4.865241</td>\n",
       "      <td>4.775817</td>\n",
       "      <td>4.871727</td>\n",
       "      <td>4.866869</td>\n",
       "      <td>4.643221</td>\n",
       "      <td>4.571448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Country Name      1995      1996      1997      1998  \\\n",
       "0                      Arab World  2.004868  2.014602  2.071309  2.177712   \n",
       "1          Caribbean small states  2.801518  2.856684  2.997157  2.989451   \n",
       "2  Central Europe and the Baltics  4.678528  4.753209  4.604574  4.499988   \n",
       "3      Early-demographic dividend  2.203164  2.156632  2.227311  2.364100   \n",
       "4             East Asia & Pacific  4.429090  4.203152  4.244351  4.453984   \n",
       "\n",
       "       1999      2000      2001      2002      2003  ...      2005      2006  \\\n",
       "0  2.331000  2.333596  2.588751  2.540238  2.450415  ...  2.134281  2.133038   \n",
       "1  2.767858  2.826752  2.679969  2.888693  2.740593  ...  2.629580  2.650900   \n",
       "2  4.679082  4.539711  4.666272  4.900196  5.100249  ...  4.970861  4.841450   \n",
       "3  2.454394  2.450555  2.527081  2.347702  2.363263  ...  2.337347  2.369884   \n",
       "4  4.626920  4.688849  4.684790  4.613537  4.635098  ...  4.566215  4.367146   \n",
       "\n",
       "       2007      2008      2009      2010      2011      2012      2013  \\\n",
       "0  2.166872  2.101233  2.830067  2.489631  2.539570  2.711262  2.895427   \n",
       "1  2.790665  2.822913  3.408651  3.264064  3.087653  3.314303  3.318432   \n",
       "2  4.809057  5.054785  5.394921  5.284380  5.096212  5.041317  5.029266   \n",
       "3  2.385251  2.405126  2.701260  2.507131  2.495491  2.497340  2.586701   \n",
       "4  4.297394  4.434848  4.865241  4.775817  4.871727  4.866869  4.643221   \n",
       "\n",
       "       2014  \n",
       "0  3.073161  \n",
       "1  3.260012  \n",
       "2  5.017717  \n",
       "3  2.665603  \n",
       "4  4.571448  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "data = get_data('public_health')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Initialize Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_05953c58_d32c_11ea_8998_5076af549583\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row0_col0\" class=\"data row0 col0\" >session_id </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row1_col0\" class=\"data row1 col0\" >Original Data </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row1_col1\" class=\"data row1 col1\" >(224, 21)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row2_col0\" class=\"data row2 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row3_col0\" class=\"data row3 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row3_col1\" class=\"data row3 col1\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row4_col0\" class=\"data row4 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row4_col1\" class=\"data row4 col1\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row5_col0\" class=\"data row5 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row5_col1\" class=\"data row5 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row6_col0\" class=\"data row6 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row7_col0\" class=\"data row7 col0\" >Transformed Data </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row7_col1\" class=\"data row7 col1\" >(224, 20)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row8_col0\" class=\"data row8 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row8_col1\" class=\"data row8 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row9_col0\" class=\"data row9 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row9_col1\" class=\"data row9 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row10_col0\" class=\"data row10 col0\" >Normalize </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row10_col1\" class=\"data row10 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row11_col0\" class=\"data row11 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row11_col1\" class=\"data row11 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row12_col0\" class=\"data row12 col0\" >Transformation </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row13_col0\" class=\"data row13 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row13_col1\" class=\"data row13 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row14_col0\" class=\"data row14 col0\" >PCA </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row15_col0\" class=\"data row15 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row15_col1\" class=\"data row15 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row16_col0\" class=\"data row16 col0\" >PCA components </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row17_col0\" class=\"data row17 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row18_col0\" class=\"data row18 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row19_col0\" class=\"data row19 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row19_col1\" class=\"data row19 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row20_col0\" class=\"data row20 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row20_col1\" class=\"data row20 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row21_col0\" class=\"data row21 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row21_col1\" class=\"data row21 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row22_col0\" class=\"data row22 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row22_col1\" class=\"data row22 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_05953c58_d32c_11ea_8998_5076af549583level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row23_col0\" class=\"data row23 col0\" >Group Features </td>\n",
       "                        <td id=\"T_05953c58_d32c_11ea_8998_5076af549583row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23648434c08>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020/07/31 08:47:41 WARNING mlflow.tracking.context.git_context: Failed to import Git (the Git executable is probably not on your PATH), so Git SHA is not available. Error: No module named 'repository'\n"
     ]
    }
   ],
   "source": [
    "from pycaret.clustering import *\n",
    "clu1 = setup(data, ignore_features = ['Country Name'], session_id=123, log_experiment=True, log_plots = True, \n",
    "             experiment_name='health1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Reference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kmeans</th>\n",
       "      <td>K-Means Clustering</td>\n",
       "      <td>sklearn.cluster.KMeans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ap</th>\n",
       "      <td>Affinity Propagation</td>\n",
       "      <td>sklearn.cluster.AffinityPropagation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meanshift</th>\n",
       "      <td>Mean shift Clustering</td>\n",
       "      <td>sklearn.cluster.MeanShift</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sc</th>\n",
       "      <td>Spectral Clustering</td>\n",
       "      <td>sklearn.cluster.SpectralClustering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hclust</th>\n",
       "      <td>Agglomerative Clustering</td>\n",
       "      <td>sklearn.cluster.AgglomerativeClustering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dbscan</th>\n",
       "      <td>Density-Based Spatial Clustering</td>\n",
       "      <td>sklearn.cluster.DBSCAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>optics</th>\n",
       "      <td>OPTICS Clustering</td>\n",
       "      <td>sklearn.cluster.OPTICS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>birch</th>\n",
       "      <td>Birch Clustering</td>\n",
       "      <td>sklearn.cluster.Birch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kmodes</th>\n",
       "      <td>K-Modes Clustering</td>\n",
       "      <td>git/nicodv/kmodes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Name  \\\n",
       "ID                                            \n",
       "kmeans                   K-Means Clustering   \n",
       "ap                     Affinity Propagation   \n",
       "meanshift             Mean shift Clustering   \n",
       "sc                      Spectral Clustering   \n",
       "hclust             Agglomerative Clustering   \n",
       "dbscan     Density-Based Spatial Clustering   \n",
       "optics                    OPTICS Clustering   \n",
       "birch                      Birch Clustering   \n",
       "kmodes                   K-Modes Clustering   \n",
       "\n",
       "                                         Reference  \n",
       "ID                                                  \n",
       "kmeans                      sklearn.cluster.KMeans  \n",
       "ap             sklearn.cluster.AffinityPropagation  \n",
       "meanshift                sklearn.cluster.MeanShift  \n",
       "sc              sklearn.cluster.SpectralClustering  \n",
       "hclust     sklearn.cluster.AgglomerativeClustering  \n",
       "dbscan                      sklearn.cluster.DBSCAN  \n",
       "optics                      sklearn.cluster.OPTICS  \n",
       "birch                        sklearn.cluster.Birch  \n",
       "kmodes                           git/nicodv/kmodes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Silhouette</th>\n",
       "      <td>0.4335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calinski-Harabasz</th>\n",
       "      <td>322.9575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Davies-Bouldin</th>\n",
       "      <td>0.7471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Metric\n",
       "Silhouette           0.4335\n",
       "Calinski-Harabasz  322.9575\n",
       "Davies-Bouldin       0.7471"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = create_model('kmeans', num_clusters = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Silhouette</th>\n",
       "      <td>-0.3632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calinski-Harabasz</th>\n",
       "      <td>1.2468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Davies-Bouldin</th>\n",
       "      <td>1.2297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Metric\n",
       "Silhouette        -0.3632\n",
       "Calinski-Harabasz  1.2468\n",
       "Davies-Bouldin     1.2297"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmodes = create_model('kmodes', num_clusters = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Assign Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>...</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>2.004868</td>\n",
       "      <td>2.014602</td>\n",
       "      <td>2.071309</td>\n",
       "      <td>2.177712</td>\n",
       "      <td>2.331000</td>\n",
       "      <td>2.333596</td>\n",
       "      <td>2.588751</td>\n",
       "      <td>2.540238</td>\n",
       "      <td>2.450415</td>\n",
       "      <td>...</td>\n",
       "      <td>2.133038</td>\n",
       "      <td>2.166872</td>\n",
       "      <td>2.101233</td>\n",
       "      <td>2.830067</td>\n",
       "      <td>2.489631</td>\n",
       "      <td>2.539570</td>\n",
       "      <td>2.711262</td>\n",
       "      <td>2.895427</td>\n",
       "      <td>3.073161</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caribbean small states</td>\n",
       "      <td>2.801518</td>\n",
       "      <td>2.856684</td>\n",
       "      <td>2.997157</td>\n",
       "      <td>2.989451</td>\n",
       "      <td>2.767858</td>\n",
       "      <td>2.826752</td>\n",
       "      <td>2.679969</td>\n",
       "      <td>2.888693</td>\n",
       "      <td>2.740593</td>\n",
       "      <td>...</td>\n",
       "      <td>2.650900</td>\n",
       "      <td>2.790665</td>\n",
       "      <td>2.822913</td>\n",
       "      <td>3.408651</td>\n",
       "      <td>3.264064</td>\n",
       "      <td>3.087653</td>\n",
       "      <td>3.314303</td>\n",
       "      <td>3.318432</td>\n",
       "      <td>3.260012</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central Europe and the Baltics</td>\n",
       "      <td>4.678528</td>\n",
       "      <td>4.753209</td>\n",
       "      <td>4.604574</td>\n",
       "      <td>4.499988</td>\n",
       "      <td>4.679082</td>\n",
       "      <td>4.539711</td>\n",
       "      <td>4.666272</td>\n",
       "      <td>4.900196</td>\n",
       "      <td>5.100249</td>\n",
       "      <td>...</td>\n",
       "      <td>4.841450</td>\n",
       "      <td>4.809057</td>\n",
       "      <td>5.054785</td>\n",
       "      <td>5.394921</td>\n",
       "      <td>5.284380</td>\n",
       "      <td>5.096212</td>\n",
       "      <td>5.041317</td>\n",
       "      <td>5.029266</td>\n",
       "      <td>5.017717</td>\n",
       "      <td>Cluster 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Early-demographic dividend</td>\n",
       "      <td>2.203164</td>\n",
       "      <td>2.156632</td>\n",
       "      <td>2.227311</td>\n",
       "      <td>2.364100</td>\n",
       "      <td>2.454394</td>\n",
       "      <td>2.450555</td>\n",
       "      <td>2.527081</td>\n",
       "      <td>2.347702</td>\n",
       "      <td>2.363263</td>\n",
       "      <td>...</td>\n",
       "      <td>2.369884</td>\n",
       "      <td>2.385251</td>\n",
       "      <td>2.405126</td>\n",
       "      <td>2.701260</td>\n",
       "      <td>2.507131</td>\n",
       "      <td>2.495491</td>\n",
       "      <td>2.497340</td>\n",
       "      <td>2.586701</td>\n",
       "      <td>2.665603</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>4.429090</td>\n",
       "      <td>4.203152</td>\n",
       "      <td>4.244351</td>\n",
       "      <td>4.453984</td>\n",
       "      <td>4.626920</td>\n",
       "      <td>4.688849</td>\n",
       "      <td>4.684790</td>\n",
       "      <td>4.613537</td>\n",
       "      <td>4.635098</td>\n",
       "      <td>...</td>\n",
       "      <td>4.367146</td>\n",
       "      <td>4.297394</td>\n",
       "      <td>4.434848</td>\n",
       "      <td>4.865241</td>\n",
       "      <td>4.775817</td>\n",
       "      <td>4.871727</td>\n",
       "      <td>4.866869</td>\n",
       "      <td>4.643221</td>\n",
       "      <td>4.571448</td>\n",
       "      <td>Cluster 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Country Name      1995      1996      1997      1998  \\\n",
       "0                      Arab World  2.004868  2.014602  2.071309  2.177712   \n",
       "1          Caribbean small states  2.801518  2.856684  2.997157  2.989451   \n",
       "2  Central Europe and the Baltics  4.678528  4.753209  4.604574  4.499988   \n",
       "3      Early-demographic dividend  2.203164  2.156632  2.227311  2.364100   \n",
       "4             East Asia & Pacific  4.429090  4.203152  4.244351  4.453984   \n",
       "\n",
       "       1999      2000      2001      2002      2003  ...      2006      2007  \\\n",
       "0  2.331000  2.333596  2.588751  2.540238  2.450415  ...  2.133038  2.166872   \n",
       "1  2.767858  2.826752  2.679969  2.888693  2.740593  ...  2.650900  2.790665   \n",
       "2  4.679082  4.539711  4.666272  4.900196  5.100249  ...  4.841450  4.809057   \n",
       "3  2.454394  2.450555  2.527081  2.347702  2.363263  ...  2.369884  2.385251   \n",
       "4  4.626920  4.688849  4.684790  4.613537  4.635098  ...  4.367146  4.297394   \n",
       "\n",
       "       2008      2009      2010      2011      2012      2013      2014  \\\n",
       "0  2.101233  2.830067  2.489631  2.539570  2.711262  2.895427  3.073161   \n",
       "1  2.822913  3.408651  3.264064  3.087653  3.314303  3.318432  3.260012   \n",
       "2  5.054785  5.394921  5.284380  5.096212  5.041317  5.029266  5.017717   \n",
       "3  2.405126  2.701260  2.507131  2.495491  2.497340  2.586701  2.665603   \n",
       "4  4.434848  4.865241  4.775817  4.871727  4.866869  4.643221  4.571448   \n",
       "\n",
       "     Cluster  \n",
       "0  Cluster 2  \n",
       "1  Cluster 2  \n",
       "2  Cluster 0  \n",
       "3  Cluster 2  \n",
       "4  Cluster 0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_results = assign_model(kmeans)\n",
    "kmeans_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Analyze Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "customdata": [
          [
           "Honduras"
          ],
          [
           "Dominica"
          ],
          [
           "Djibouti"
          ],
          [
           "Samoa"
          ],
          [
           "San Marino"
          ],
          [
           "Sao Tome and Principe"
          ],
          [
           "Colombia"
          ],
          [
           "Chile"
          ],
          [
           "Cabo Verde"
          ],
          [
           "Solomon Islands"
          ],
          [
           "Bulgaria"
          ],
          [
           "South Africa"
          ],
          [
           "Brazil"
          ],
          [
           "Botswana"
          ],
          [
           "Bolivia"
          ],
          [
           "Bhutan"
          ],
          [
           "Belarus"
          ],
          [
           "Barbados"
          ],
          [
           "St. Lucia"
          ],
          [
           "St. Vincent and the Grenadines"
          ],
          [
           "Russian Federation"
          ],
          [
           "Suriname"
          ],
          [
           "Romania"
          ],
          [
           "Estonia"
          ],
          [
           "Moldova"
          ],
          [
           "Malawi"
          ],
          [
           "Monaco"
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(kmeans, plot = 'elbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(kmeans, plot = 'silhouette')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(kmeans, plot = 'distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
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       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
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     "output_type": "display_data"
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\"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"categoryarray\": [\"Cluster 0\", \"Cluster 1\", \"Cluster 2\", \"Cluster 3\"], \"categoryorder\": \"array\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Cluster\"}}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"matches\": \"x\", \"showgrid\": true, \"showticklabels\": false}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 0.7326], \"title\": {\"text\": \"count\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.7426, 1.0], \"matches\": \"y2\", \"showgrid\": false, \"showline\": false, \"showticklabels\": false, \"ticks\": \"\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('03bf0c09-50b4-4af8-9d78-a2bdb3c93fe3');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(kmeans, plot = 'distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Predict Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>...</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>2.004868</td>\n",
       "      <td>2.014602</td>\n",
       "      <td>2.071309</td>\n",
       "      <td>2.177712</td>\n",
       "      <td>2.331000</td>\n",
       "      <td>2.333596</td>\n",
       "      <td>2.588751</td>\n",
       "      <td>2.540238</td>\n",
       "      <td>2.450415</td>\n",
       "      <td>...</td>\n",
       "      <td>2.133038</td>\n",
       "      <td>2.166872</td>\n",
       "      <td>2.101233</td>\n",
       "      <td>2.830067</td>\n",
       "      <td>2.489631</td>\n",
       "      <td>2.539570</td>\n",
       "      <td>2.711262</td>\n",
       "      <td>2.895427</td>\n",
       "      <td>3.073161</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caribbean small states</td>\n",
       "      <td>2.801518</td>\n",
       "      <td>2.856684</td>\n",
       "      <td>2.997157</td>\n",
       "      <td>2.989451</td>\n",
       "      <td>2.767858</td>\n",
       "      <td>2.826752</td>\n",
       "      <td>2.679969</td>\n",
       "      <td>2.888693</td>\n",
       "      <td>2.740593</td>\n",
       "      <td>...</td>\n",
       "      <td>2.650900</td>\n",
       "      <td>2.790665</td>\n",
       "      <td>2.822913</td>\n",
       "      <td>3.408651</td>\n",
       "      <td>3.264064</td>\n",
       "      <td>3.087653</td>\n",
       "      <td>3.314303</td>\n",
       "      <td>3.318432</td>\n",
       "      <td>3.260012</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central Europe and the Baltics</td>\n",
       "      <td>4.678528</td>\n",
       "      <td>4.753209</td>\n",
       "      <td>4.604574</td>\n",
       "      <td>4.499988</td>\n",
       "      <td>4.679082</td>\n",
       "      <td>4.539711</td>\n",
       "      <td>4.666272</td>\n",
       "      <td>4.900196</td>\n",
       "      <td>5.100249</td>\n",
       "      <td>...</td>\n",
       "      <td>4.841450</td>\n",
       "      <td>4.809057</td>\n",
       "      <td>5.054785</td>\n",
       "      <td>5.394921</td>\n",
       "      <td>5.284380</td>\n",
       "      <td>5.096212</td>\n",
       "      <td>5.041317</td>\n",
       "      <td>5.029266</td>\n",
       "      <td>5.017717</td>\n",
       "      <td>Cluster 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Early-demographic dividend</td>\n",
       "      <td>2.203164</td>\n",
       "      <td>2.156632</td>\n",
       "      <td>2.227311</td>\n",
       "      <td>2.364100</td>\n",
       "      <td>2.454394</td>\n",
       "      <td>2.450555</td>\n",
       "      <td>2.527081</td>\n",
       "      <td>2.347702</td>\n",
       "      <td>2.363263</td>\n",
       "      <td>...</td>\n",
       "      <td>2.369884</td>\n",
       "      <td>2.385251</td>\n",
       "      <td>2.405126</td>\n",
       "      <td>2.701260</td>\n",
       "      <td>2.507131</td>\n",
       "      <td>2.495491</td>\n",
       "      <td>2.497340</td>\n",
       "      <td>2.586701</td>\n",
       "      <td>2.665603</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>4.429090</td>\n",
       "      <td>4.203152</td>\n",
       "      <td>4.244351</td>\n",
       "      <td>4.453984</td>\n",
       "      <td>4.626920</td>\n",
       "      <td>4.688849</td>\n",
       "      <td>4.684790</td>\n",
       "      <td>4.613537</td>\n",
       "      <td>4.635098</td>\n",
       "      <td>...</td>\n",
       "      <td>4.367146</td>\n",
       "      <td>4.297394</td>\n",
       "      <td>4.434848</td>\n",
       "      <td>4.865241</td>\n",
       "      <td>4.775817</td>\n",
       "      <td>4.871727</td>\n",
       "      <td>4.866869</td>\n",
       "      <td>4.643221</td>\n",
       "      <td>4.571448</td>\n",
       "      <td>Cluster 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Country Name      1995      1996      1997      1998  \\\n",
       "0                      Arab World  2.004868  2.014602  2.071309  2.177712   \n",
       "1          Caribbean small states  2.801518  2.856684  2.997157  2.989451   \n",
       "2  Central Europe and the Baltics  4.678528  4.753209  4.604574  4.499988   \n",
       "3      Early-demographic dividend  2.203164  2.156632  2.227311  2.364100   \n",
       "4             East Asia & Pacific  4.429090  4.203152  4.244351  4.453984   \n",
       "\n",
       "       1999      2000      2001      2002      2003  ...      2006      2007  \\\n",
       "0  2.331000  2.333596  2.588751  2.540238  2.450415  ...  2.133038  2.166872   \n",
       "1  2.767858  2.826752  2.679969  2.888693  2.740593  ...  2.650900  2.790665   \n",
       "2  4.679082  4.539711  4.666272  4.900196  5.100249  ...  4.841450  4.809057   \n",
       "3  2.454394  2.450555  2.527081  2.347702  2.363263  ...  2.369884  2.385251   \n",
       "4  4.626920  4.688849  4.684790  4.613537  4.635098  ...  4.367146  4.297394   \n",
       "\n",
       "       2008      2009      2010      2011      2012      2013      2014  \\\n",
       "0  2.101233  2.830067  2.489631  2.539570  2.711262  2.895427  3.073161   \n",
       "1  2.822913  3.408651  3.264064  3.087653  3.314303  3.318432  3.260012   \n",
       "2  5.054785  5.394921  5.284380  5.096212  5.041317  5.029266  5.017717   \n",
       "3  2.405126  2.701260  2.507131  2.495491  2.497340  2.586701  2.665603   \n",
       "4  4.434848  4.865241  4.775817  4.871727  4.866869  4.643221  4.571448   \n",
       "\n",
       "     Cluster  \n",
       "0  Cluster 2  \n",
       "1  Cluster 2  \n",
       "2  Cluster 0  \n",
       "3  Cluster 2  \n",
       "4  Cluster 0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_new = predict_model(kmeans, data=data)\n",
    "pred_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Save / Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(kmeans, model_name='kmeans')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n",
      "[Pipeline(memory=None,\n",
      "         steps=[('dtypes',\n",
      "                 DataTypes_Auto_infer(categorical_features=[],\n",
      "                                      display_types=True,\n",
      "                                      features_todrop=['Country Name'],\n",
      "                                      ml_usecase='regression',\n",
      "                                      numerical_features=[],\n",
      "                                      target='dummy_target',\n",
      "                                      time_features=[])),\n",
      "                ('imputer',\n",
      "                 Simple_Imputer(categorical_strategy='not_available',\n",
      "                                numeric_strategy='mean',\n",
      "                                target_variable=None)),\n",
      "                ('new_levels1',\n",
      "                 New_...\n",
      "                                                    target='dummy_target')),\n",
      "                ('feature_time',\n",
      "                 Make_Time_Features(list_of_features=None, time_feature=[])),\n",
      "                ('group', Empty()), ('scaling', Empty()),\n",
      "                ('P_transform', Empty()), ('binn', Empty()),\n",
      "                ('fix_perfect', Empty()), ('rem_outliers', Empty()),\n",
      "                ('dummy', Dummify(target='dummy_target')),\n",
      "                ('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\n",
      "                ('pca', Empty())],\n",
      "         verbose=False), KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
      "       n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\n",
      "       random_state=123, tol=0.0001, verbose=0)]\n"
     ]
    }
   ],
   "source": [
    "loaded_kmeans = load_model('kmeans')\n",
    "print(loaded_kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e13cc33d-9b11-4e88-a365-93bce126d9e2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e13cc33d-9b11-4e88-a365-93bce126d9e2\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True,\n",
       "                                      features_todrop=['Country Name'],\n",
       "                                      ml_usecase='regression',\n",
       "                                      numerical_features=[],\n",
       "                                      target='dummy_target',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='not_available',\n",
       "                                numeric_strategy='mean',\n",
       "                                target_variable=None)),\n",
       "                ('new_levels1',\n",
       "                 New_...\n",
       "                                                    target='dummy_target')),\n",
       "                ('feature_time',\n",
       "                 Make_Time_Features(list_of_features=None, time_feature=[])),\n",
       "                ('group', Empty()), ('scaling', Empty()),\n",
       "                ('P_transform', Empty()), ('binn', Empty()),\n",
       "                ('fix_perfect', Empty()), ('rem_outliers', Empty()),\n",
       "                ('dummy', Dummify(target='dummy_target')),\n",
       "                ('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\n",
       "                ('pca', Empty())],\n",
       "         verbose=False)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"2d053f8e-ddfe-47f8-8568-3304def0828d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"2d053f8e-ddfe-47f8-8568-3304def0828d\">DataTypes_Auto_infer</label><div class=\"sk-toggleable__content\"><pre>DataTypes_Auto_infer(features_todrop=['Country Name'], ml_usecase='regression',\n",
       "                     target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5d94da30-28be-4e28-ab49-295410271ddc\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"5d94da30-28be-4e28-ab49-295410271ddc\">Simple_Imputer</label><div class=\"sk-toggleable__content\"><pre>Simple_Imputer(categorical_strategy='not_available', numeric_strategy='mean',\n",
       "               target_variable=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"382b7432-2a84-40e3-9df9-e6b2d4eb08e8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"382b7432-2a84-40e3-9df9-e6b2d4eb08e8\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"53131778-ce77-4a72-8a16-cec360702c76\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"53131778-ce77-4a72-8a16-cec360702c76\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"939bf9d6-707d-4553-85a4-490a7281419e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"939bf9d6-707d-4553-85a4-490a7281419e\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"39bd1050-4265-41c0-9b89-74fbea8fdf18\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"39bd1050-4265-41c0-9b89-74fbea8fdf18\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"fa786f12-fce3-4c1e-b5e1-50051f26e91e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"fa786f12-fce3-4c1e-b5e1-50051f26e91e\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"af2c29cc-128e-4fc5-9b5e-94790ad06cf8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"af2c29cc-128e-4fc5-9b5e-94790ad06cf8\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"231522fc-f440-4223-a555-9d84799adba4\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"231522fc-f440-4223-a555-9d84799adba4\">Make_Time_Features</label><div class=\"sk-toggleable__content\"><pre>Make_Time_Features(list_of_features=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"479cd0e1-ae05-40c5-adaa-91803ad603b9\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"479cd0e1-ae05-40c5-adaa-91803ad603b9\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bcbbeb81-d5bd-4689-8dcb-451e2da1ebb7\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"bcbbeb81-d5bd-4689-8dcb-451e2da1ebb7\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"2f20f4f6-4ceb-4bf9-9a5d-6bf1b1cee285\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"2f20f4f6-4ceb-4bf9-9a5d-6bf1b1cee285\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b4fb28b8-6ad9-42be-8283-82e22386d784\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b4fb28b8-6ad9-42be-8283-82e22386d784\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"99fc11de-8393-40ac-aacd-a13411db3216\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"99fc11de-8393-40ac-aacd-a13411db3216\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"0bc0a1e8-fb2b-49c0-9c98-62698e02acc3\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"0bc0a1e8-fb2b-49c0-9c98-62698e02acc3\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5c26838a-2492-4090-98fc-cd23e4c6ef91\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"5c26838a-2492-4090-98fc-cd23e4c6ef91\">Dummify</label><div class=\"sk-toggleable__content\"><pre>Dummify(target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8a9aaa55-e5ff-4d46-913d-1e2c82ec6acc\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8a9aaa55-e5ff-4d46-913d-1e2c82ec6acc\">Clean_Colum_Names</label><div class=\"sk-toggleable__content\"><pre>Clean_Colum_Names()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"99da2ccc-b3d5-47a5-a362-13b0c7133bbf\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"99da2ccc-b3d5-47a5-a362-13b0c7133bbf\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4cc8bf68-46b5-4f98-ac98-7872445b8774\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4cc8bf68-46b5-4f98-ac98-7872445b8774\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True,\n",
       "                                      features_todrop=['Country Name'],\n",
       "                                      ml_usecase='regression',\n",
       "                                      numerical_features=[],\n",
       "                                      target='dummy_target',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='not_available',\n",
       "                                numeric_strategy='mean',\n",
       "                                target_variable=None)),\n",
       "                ('new_levels1',\n",
       "                 New_...\n",
       "                                                    target='dummy_target')),\n",
       "                ('feature_time',\n",
       "                 Make_Time_Features(list_of_features=None, time_feature=[])),\n",
       "                ('group', Empty()), ('scaling', Empty()),\n",
       "                ('P_transform', Empty()), ('binn', Empty()),\n",
       "                ('fix_perfect', Empty()), ('rem_outliers', Empty()),\n",
       "                ('dummy', Dummify(target='dummy_target')),\n",
       "                ('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\n",
       "                ('pca', Empty())],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='diagram')\n",
    "loaded_kmeans[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='text')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Deploy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Succesfully Deployed on AWS S3\n"
     ]
    }
   ],
   "source": [
    "deploy_model(kmeans, model_name = 'kmeans-aws', authentication = {'bucket' : 'pycaret-test'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Get Config / Set Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.004868</td>\n",
       "      <td>2.014602</td>\n",
       "      <td>2.071309</td>\n",
       "      <td>2.177712</td>\n",
       "      <td>2.331000</td>\n",
       "      <td>2.333596</td>\n",
       "      <td>2.588751</td>\n",
       "      <td>2.540238</td>\n",
       "      <td>2.450415</td>\n",
       "      <td>2.314914</td>\n",
       "      <td>2.134281</td>\n",
       "      <td>2.133038</td>\n",
       "      <td>2.166872</td>\n",
       "      <td>2.101233</td>\n",
       "      <td>2.830067</td>\n",
       "      <td>2.489631</td>\n",
       "      <td>2.539570</td>\n",
       "      <td>2.711262</td>\n",
       "      <td>2.895427</td>\n",
       "      <td>3.073161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.801518</td>\n",
       "      <td>2.856684</td>\n",
       "      <td>2.997157</td>\n",
       "      <td>2.989451</td>\n",
       "      <td>2.767858</td>\n",
       "      <td>2.826752</td>\n",
       "      <td>2.679969</td>\n",
       "      <td>2.888693</td>\n",
       "      <td>2.740593</td>\n",
       "      <td>2.845971</td>\n",
       "      <td>2.629580</td>\n",
       "      <td>2.650900</td>\n",
       "      <td>2.790665</td>\n",
       "      <td>2.822913</td>\n",
       "      <td>3.408651</td>\n",
       "      <td>3.264064</td>\n",
       "      <td>3.087653</td>\n",
       "      <td>3.314303</td>\n",
       "      <td>3.318432</td>\n",
       "      <td>3.260012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.678528</td>\n",
       "      <td>4.753209</td>\n",
       "      <td>4.604574</td>\n",
       "      <td>4.499988</td>\n",
       "      <td>4.679082</td>\n",
       "      <td>4.539711</td>\n",
       "      <td>4.666272</td>\n",
       "      <td>4.900196</td>\n",
       "      <td>5.100249</td>\n",
       "      <td>4.913629</td>\n",
       "      <td>4.970861</td>\n",
       "      <td>4.841450</td>\n",
       "      <td>4.809057</td>\n",
       "      <td>5.054785</td>\n",
       "      <td>5.394921</td>\n",
       "      <td>5.284380</td>\n",
       "      <td>5.096212</td>\n",
       "      <td>5.041317</td>\n",
       "      <td>5.029266</td>\n",
       "      <td>5.017717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.203164</td>\n",
       "      <td>2.156632</td>\n",
       "      <td>2.227311</td>\n",
       "      <td>2.364100</td>\n",
       "      <td>2.454394</td>\n",
       "      <td>2.450555</td>\n",
       "      <td>2.527081</td>\n",
       "      <td>2.347702</td>\n",
       "      <td>2.363263</td>\n",
       "      <td>2.346824</td>\n",
       "      <td>2.337347</td>\n",
       "      <td>2.369884</td>\n",
       "      <td>2.385251</td>\n",
       "      <td>2.405126</td>\n",
       "      <td>2.701260</td>\n",
       "      <td>2.507131</td>\n",
       "      <td>2.495491</td>\n",
       "      <td>2.497340</td>\n",
       "      <td>2.586701</td>\n",
       "      <td>2.665603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.429090</td>\n",
       "      <td>4.203152</td>\n",
       "      <td>4.244351</td>\n",
       "      <td>4.453984</td>\n",
       "      <td>4.626920</td>\n",
       "      <td>4.688849</td>\n",
       "      <td>4.684790</td>\n",
       "      <td>4.613537</td>\n",
       "      <td>4.635098</td>\n",
       "      <td>4.626696</td>\n",
       "      <td>4.566215</td>\n",
       "      <td>4.367146</td>\n",
       "      <td>4.297394</td>\n",
       "      <td>4.434848</td>\n",
       "      <td>4.865241</td>\n",
       "      <td>4.775817</td>\n",
       "      <td>4.871727</td>\n",
       "      <td>4.866869</td>\n",
       "      <td>4.643221</td>\n",
       "      <td>4.571448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       1995      1996      1997      1998      1999      2000      2001  \\\n",
       "0  2.004868  2.014602  2.071309  2.177712  2.331000  2.333596  2.588751   \n",
       "1  2.801518  2.856684  2.997157  2.989451  2.767858  2.826752  2.679969   \n",
       "2  4.678528  4.753209  4.604574  4.499988  4.679082  4.539711  4.666272   \n",
       "3  2.203164  2.156632  2.227311  2.364100  2.454394  2.450555  2.527081   \n",
       "4  4.429090  4.203152  4.244351  4.453984  4.626920  4.688849  4.684790   \n",
       "\n",
       "       2002      2003      2004      2005      2006      2007      2008  \\\n",
       "0  2.540238  2.450415  2.314914  2.134281  2.133038  2.166872  2.101233   \n",
       "1  2.888693  2.740593  2.845971  2.629580  2.650900  2.790665  2.822913   \n",
       "2  4.900196  5.100249  4.913629  4.970861  4.841450  4.809057  5.054785   \n",
       "3  2.347702  2.363263  2.346824  2.337347  2.369884  2.385251  2.405126   \n",
       "4  4.613537  4.635098  4.626696  4.566215  4.367146  4.297394  4.434848   \n",
       "\n",
       "       2009      2010      2011      2012      2013      2014  \n",
       "0  2.830067  2.489631  2.539570  2.711262  2.895427  3.073161  \n",
       "1  3.408651  3.264064  3.087653  3.314303  3.318432  3.260012  \n",
       "2  5.394921  5.284380  5.096212  5.041317  5.029266  5.017717  \n",
       "3  2.701260  2.507131  2.495491  2.497340  2.586701  2.665603  \n",
       "4  4.865241  4.775817  4.871727  4.866869  4.643221  4.571448  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = get_config('X')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.clustering import set_config\n",
    "set_config('seed', 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. MLFlow UI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!mlflow ui"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End\n",
    "Thank you. For more information / tutorials on PyCaret, please visit https://www.pycaret.org"
   ]
  }
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